• Loading metrics

Diagnostic performance of congestion score index evaluated from chest radiography for acute heart failure in the emergency department: A retrospective analysis from the PARADISE cohort

Diagnostic performance of congestion score index evaluated from chest radiography for acute heart failure in the emergency department: A retrospective analysis from the PARADISE cohort

  • Masatake Kobayashi, 
  • Amine Douair, 
  • Kevin Duarte, 
  • Déborah Jaeger, 
  • Gaetan Giacomin, 
  • Adrien Bassand, 
  • Victor Jeangeorges, 
  • Laure Abensur Vuillaume, 
  • Gregoire Preud'homme, 
  • Olivier Huttin



Congestion score index (CSI), a semiquantitative evaluation of congestion on chest radiography (CXR), is associated with outcome in patients with heart failure (HF). However, its diagnostic value in patients admitted for acute dyspnea has yet to be evaluated.

Methods and findings

The diagnostic value of CSI for acute HF (AHF; adjudicated from patients’ discharge files) was studied in the Pathway of dyspneic patients in Emergency (PARADISE) cohort, including patients aged 18 years or older admitted for acute dyspnea in the emergency department (ED) of the Nancy University Hospital (France) between January 1, 2015 and December 31, 2015. CSI (ranging from 0 to 3) was evaluated using a semiquantitative method on CXR in consecutive patients admitted for acute dyspnea in the ED. Results were validated in independent cohorts (N = 224). Of 1,333 patients, mean (standard deviation [SD]) age was 72.0 (18.5) years, 686 (51.5%) were men, and mean (SD) CSI was 1.42 (0.79). Patients with higher CSI had more cardiovascular comorbidities, more severe congestion, higher b-type natriuretic peptide (BNP), poorer renal function, and more respiratory acidosis. AHF was diagnosed in 289 (21.7%) patients. CSI was significantly associated with AHF diagnosis (adjusted odds ratio [OR] for 0.1 unit CSI increase 1.19, 95% CI 1.16–1.22, p < 0.001) after adjustment for clinical-based diagnostic score including age, comorbidity burden, dyspnea, and clinical congestion. The diagnostic accuracy of CSI for AHF was >0.80, whether alone (area under the receiver operating characteristic curve [AUROC] 0.84, 95% CI 0.82–0.86) or in addition to the clinical model (AUROC 0.87, 95% CI 0.85–0.90). CSI improved diagnostic accuracy on top of clinical variables (net reclassification improvement [NRI] = 94.9%) and clinical variables plus BNP (NRI = 55.0%). Similar diagnostic accuracy was observed in the validation cohorts (AUROC 0.75, 95% CI 0.68–0.82). The key limitation of our derivation cohort was its single-center and retrospective nature, which was counterbalanced by the validation in the independent cohorts.


In this study, we observed that a systematic semiquantified assessment of radiographic pulmonary congestion showed high diagnostic value for AHF in dyspneic patients. Better use of CXR may provide an inexpensive, widely, and readily available method for AHF triage in the ED.

Author summary

Why was this study done?

  • Chest radiography (CXR) is often performed in patients admitted for acute dyspnea in the emergency department (ED); however, its assessment is generally not standardized.
  • Latest guidelines for heart failure (HF) emphasize the limitations of CXR in its current use.
  • A standardized approach to quantify pulmonary congestion from CXR could potentially improve its diagnostic performance for acute HF (AHF).

What did the researchers do and find?

  • We studied the diagnostic value of a semiquantitative approach (the congestion score index [CSI]) to pulmonary congestion on CXR for AHF in a retrospective cohort study of 1,333 patients with acute dyspnea admitted to the ED.
  • This CSI was significantly associated with AHF diagnosis.
  • The CSI also improved diagnostic accuracy over clinical parameters with or without inclusion of natriuretic peptide.
  • This good diagnostic accuracy of the CSI was externally validated in independent cohorts (N = 224).

What do these findings mean?

  • The quantification of radiographic pulmonary congestion using the CSI improved diagnostic accuracy for AHF on top of clinical parameters and natriuretic peptide.


Acute heart failure (AHF) is 1 of the leading causes of acute dyspnea in the emergency department (ED) [1] and is associated with a higher risk of morbidity and mortality [2,3]. In-hospital mortality is reported to be greater than 10% [4] and has remained stable in the last 30 years. As prognosis is associated with initiation time of specific therapies [5], current guidelines emphasize the importance of early diagnosis and treatment initiation to improve clinical outcomes [5,6]. However, a minority of patients with AHF receive treatment within 1 hour of admission [5], in contradiction with current recommendations [6]. In addition, a third of AHF diagnosis are missed in the ED [6], further delaying access to care. An increasing number of better diagnostic tools for AHF are available in the ED [79]. However, there is likely room for improving the diagnostic approach to AHF from widely available routine tools including chest radiography (CXR).

CXR is a fast and inexpensive method performed systematically in the ED in patients with acute dyspnea [1012]. It is the first-line diagnostic imaging modality advocated in current guidelines [13]. However, its diagnostic accuracy for HF has been reported to be relatively low [1416]. In particular, diagnosing HF in patients with concomitant lung diseases such as chronic obstructive pulmonary disease (COPD) and pneumonia still remains a challenge [17].

A new semiquantitative approach to pulmonary congestion has recently emerged in the field of HF. Congestion score index (CSI) is a semiquantitative approach to pulmonary congestion based on a 6-zone evaluation of CXR, scoring each zone from 0 (no congestion) to 3 (intense alveolar pulmonary edema). CSI is a strong risk stratifier in patients with stable or worsening HF [1820]. However, there are little available data regarding its diagnostic value for AHF.

The aims of the present study are to investigate the diagnostic value of pulmonary congestion assessed with CSI for AHF in patients admitted for acute dyspnea in the ED and to assess its discriminative value comparatively to and on top of currently used clinical diagnostic models and natriuretic peptide measurements.


Study population

This study is reported as per the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guideline (S1 Checklist). The Pathway of dyspneic patients in Emergency (PARADISE) cohort is a retrospective cohort study including consecutive patients aged 18 years or older who were admitted for acute dyspnea in the ED of the Nancy University Hospital (France) between January 1, 2015 and December 31, 2015 as detailed previously [21,22]. The hospital’s electronic charts (resurgences) were systematically reviewed by investigators to search for the records of all patients admitted for acute dyspnea in the ED. All patients with signs or symptoms of dyspnea or requiring oxygen therapy for dyspnea during (or prior to) their ED stay were included. Patients with shock or cardiac arrest were excluded from this analysis as dyspnea was not the primary condition triggering ED admission. The PARADISE cohort is consequently a cohort of unselected consecutive patients with acute dyspnea in the ED. In the current study, a total of 1,333 dyspneic patients with available information on CXR at the ED were analyzed (S1 Fig). Demographic parameters, medical history, physical examination, laboratory findings, and treatment received in the ED were retrieved from the patients’ electronic records.

External validation was performed on the merged dataset from the HF disease management program entitled “Insuffisance CArdiaque en LORraine (ICALOR)” [23,24] (included at a different time period that the data from the Nancy University Hospital used in the derivation set) and data from the Epinal Hospital (a secondary care hospital located 70 km from the Nancy University Hospital) within the Pathway and Urgent caRe of Dyspneic Patient at the Emergency Department in LorrainE District (PURPLE) multicenter cohort (NCT03194243). Briefly, we used the data from the previously described [19] 117 patients included in the ICALOR disease management cohort during a hospitalization for acutely decompensated HF in the Cardiology Department of the Nancy University Hospital (France). We also used the data of 107 consecutive patients admitted for acute dyspnea with available CXR data and discharge diagnosis in the ED of the Epinal Hospital. This external validation set allowed us to test CSI in a significantly different setting (mixing cardiology department from a tertiary care hospital and an ED of a secondary care hospital) than our derivation set.

Under French law, no formal Institutional Review Board approval is required for data extraction from patient medical records in single-center cohorts (the PARADISE and ICALOR cohorts). For the PURPLE cohort, patients were informed through a notice at admission and could refuse their inclusion in the study, although no formal consent was required in keeping with the framework of the Commission Nationale de l’Informatique et des Libertés (CNIL). The PARADISE cohort was recorded by the local hospital corresponding agent of the CNIL (Number R2016-08) and was registered on (NCT02800122). The PURPLE cohort was approved by an ethical board (“Comité de Protection des personnes”—Number 2016–63, ID RCB 2016-A01877-44) and CNIL (Number DR-2017-098) and was registered on (NCT03194243).

Diagnosis of heart failure

HF was diagnosed according to the European Society of Cardiology (ESC) guidelines [25]. Diagnosis of AHF was coded independently by 2 medical physicians (GG and TH) according to the ESC guidelines [25]. Each physician had access to all ED medical charts as well as additional hospital admission test results and records (e.g., echocardiography, natriuretic peptide level, and patient response to diuretic/bronchodilator therapy) but were blinded to CSI quantification on CXR. Homogenous coding was ensured by a trained senior physician (TC). Importantly, to determine the discharge diagnosis, we focused on the main cause of acute dyspnea rather than background medical history or coexisting conditions.

Radiographic congestion score index

Radiographic CSI was used to quantify the severity of pulmonary congestion in CXR as previously published [18,19]. After dividing the lung field into 6 topographical zones, each area was assessed as follows: Score 0, no congestion sign; Score 1, cephalization (superior area), perihilar haze or perivascular/peribronchial cuffing, or Kerley A lines (middle area), Kerley B, or C lines (inferior area); Score 2, interstitial or localized/mild alveolar pulmonary edema; Score 3, intense alveolar pulmonary edema (Fig 1). To enhance the reproducibility of the severity of confluent edema, a portion of the divided lung fields which was visually similar to the cardiac silhouette was regarded as an intense zone, whereas the field with weaker density was regarded as a mildly intense zone. Lung areas were not scored when more than one-third of the divided lung fields were occupied by pleural effusion (including vanishing tumor), atelectasis, or cardiac silhouette. CSI was calculated as the sum of the scores in each zone divided by the number of available zones. An examiner also assessed the presence of pneumonia, pleural effusion, cardiomegaly by cardiothoracic ratio (>50%), and the difficulty in assessing CSI.

Fig 1. Radiographic CSI.

The scoring is performed on 6 lung fields. The absence of radiographic congestion signs in a lung filed is graded as a score of “0.” Panels A and B provide examples. (A) Example: CSI = (2+3+3+3+2+1)/6 = 2.33. There is diffuse alveolar edema, appearing as intense edema in the left superior field and middle fields. (B) Example: CSI = (1+1+1+1+1+0)/6 = 0.83. Cephalization in superior fields and peribronchial and perivascular cuffing are visible in middle fields, respectively. *Confluent edema was regarded as intense edema when the density in an area of the divided lung field was visually similar to that of cardiac silhouette. CSI, congestion score index.

CXR was analyzed by a single emergency physician (AD), blinded to clinical data and discharge diagnosis, with no previous training in congestion quantification on CXR prior to that in the present study. After a short training (approximately 3 hours) using a 20-patient sample with a radiographic CSI expert (MK), intraobserver and interobserver agreements (with MK) were tested on 30 randomly selected patients, while blinded to clinical status and diagnosis. Intraclass correlation coefficients showed good reproducibility (intraobserver agreement, 0.85, 95% CI 0.71 to 0.93 and interobserver agreement 0.81, 95% CI 0.64 to 0.90).

Brest score

The Brest score was calculated for every patient based on the patients’ medical charts. This diagnostic score for AHF in dyspneic patients is based on age, comorbidities (i.e., prior history of HF, myocardial infarction, and COPD), pattern of dyspnea, ST segment abnormalities, and signs/symptoms of congestion (i.e., rales and leg edema) [26,27].

Statistical analysis

Categorical variables are expressed as frequencies (percentages) and continuous variables as means ± standard deviation (SD) or median (25th and 75th percentiles) according to the distribution of the variables. Comparisons of demographic, clinical, and biological parameters among quartiles of radiographic CSI were analyzed using χ2 tests for categorical variables and ANOVA or Kruskal–Wallis test for continuous variables. Interobserver and intraobserver agreements of CSI were assessed with the intraclass correlation coefficient.

We analyzed 1,333 dyspneic patients with 289 AHF diagnosis—which provided a sizeable statistical power to assess diagnostic performance in a multivariable setting [28]. A logistic regression model was used to assess the association of CSI with diagnosis of AHF. Multivariable analyses included relevant confounders as previously shown [21]: model 1: age, sex, body mass index (BMI), presence of hypertension, diabetes mellitus, coronary artery disease, atrial fibrillation, prior HF admission, use of angiotensin converting enzyme inhibitor/angiotensin receptor blocker, beta-blocker, diuretics, leg edema, jugular venous distension, hemoglobin, white blood cell count, and estimated glomerular filtration rate (eGFR, as calculated by the Chronic Kidney Disease Epidemiology Collaboration formula [29]) at admission; model 2: Brest score. Receiver operating characteristics (ROC) curve was used to determine the diagnostic value of CSI in AHF. All correlation coefficients of variables included in the models were less than 0.50 with CSI, suggesting the absence of important in-model collinearity.

The increase in discriminative value of the addition of CSI for AHF diagnosis on top of the aforementioned potential covariates was assessed using continuous net reclassification improvement (NRI) [30]. In addition, in 498 (37.4%) patients who had available b-type natriuretic peptide (BNP) measurements, the added value of CSI on the top of the Brest score and BNP was assessed.

This analysis on the PARADISE cohort was planned in February 2019, although no formal analysis was written. The general analysis intention was to evaluate the diagnostic value of CSI for AHF. Of note, among all patients with available data, CXR was assessed, blinded for clinical data and diagnosis, and standard statistical approaches were used. Based on recommendations made during the peer-review process, we conducted restricted cubic spline regression analysis for the association between CSI and AHF diagnosis.

All analyses were performed using R version 3.4.0 (R Development Core Team, Vienna, Austria). A 2-sided p-value < 0.05 was considered statistically significant. No imputation was performed.


Baseline characteristics

Less than 10% of the population had no available data on CXR (S1 Table). These patients were markedly younger and had less comorbidities than patients who underwent CXR. The characteristics of the PARADISE cohort population across different discharge diagnoses such as AHF, COPD, and pneumonia are depicted in S2 Table.

In a total of 1,333 patients included in this study, a half were men, mean age was 72.0 ± 18.5 years, mean BMI was 25.5 ± 5.5 kg/m2, and less than 10% had a prior admission for HF (7.1%) (Table 1). CXR was considered as difficult to interpret during CXR reviewing in 502 patients (37.7%). Mean CSI was 1.42 ± 0.79. Patients with a higher CSI had more cardiovascular risk factors, comorbidities, more frequent prior HF admission, more severe congestion, inflammation status, higher BNP, poorer renal function, and more respiratory acidosis at admission (Table 1).

Table 1. Baseline characteristics according to the radiographic CSI (quartiles).

Association of congestion score index with adjudicated discharge diagnosis of acute heart failure

In this study, 289 (21.7%) patients were diagnosed with AHF at discharge. Higher CSI was significantly associated with AHF diagnosis (odds ratio [OR] [95% CI] for a 0.1 unit increase in CSI = 1.22 [1.19 to 1.25], p < 0.001) even after adjustment for potential clinical confounders (adjusted OR [95% CI] in CSI = 1.18 [1.15 to 1.22], p < 0.001) and the Brest score (adjusted OR [95% CI] in CSI = 1.19 [1.16 to 1.22], p < 0.001). The association of CSI with AHF diagnosis using restricted cubic spline regression analysis is shown in Fig 2. CSI had a linear association with AHF diagnosis (p > 0.05 for nonlinearity), and higher CSI showed an increased risk of AHF diagnosis (OR [95% CI] for CSI score 1.0 = 4.09 [2.50 to 6.71], p < 0.001; OR [95% CI] for CSI score 1.5 = 13.92 [6.32 to 30.65], p < 0.001; OR [95% CI] for CSI score 2.0 = 37.03 [16.09 to 85.26], p < 0.001—considering CSI score 0.5 as a reference). Similar results were observed after adjustment for the Brest score (adjusted OR [95% CI] for CSI score 1.0 = 3.26 [1.93 to 5.48], p < 0.001; adjusted OR [95% CI] for CSI score 1.5 = 9.07 [4.00 to 20.59], p < 0.001; adjusted OR [95% CI] for CSI score 2.0 = 20.88 [8.83 to 49.35], p < 0.001—considering CSI score 0.5 as a reference) (Fig 2).

Fig 2. Association between CSI and discharge diagnosis of AHF.

Multivariable model included the Brest score, which was calculated from age, comorbidity burden, dyspnea, ST segment abnormalities, and clinical congestion. Dotted lines/shaded regions represent 95% CI. AHF, acute heart failure; CSI, congestion score index.

Diagnostic value of the congestion score index

In the whole population, CSI exhibited high discrimination for AHF as reflected by an area under the curve (AUC) of 0.84 (0.82 to 0.86) (Figs 3 and 4). Similarly, high AUC was observed across subgroups of age, sex, and comorbidities (obesity and COPD) or associated diagnosis (pneumonia and pleural effusion) (Fig 3). In contrast, subgroups without cardiomegaly assessed by CXR had higher AUC compared to those with cardiomegaly (AUC [95% CI] = 0.85 [0.81 to 0.89] versus 0.75 [0.70 to 0.79], respectively). In addition, the diagnostic value of CSI was influenced by the patient’s position (AUC [95% CI] = 0.83 [0.80 to 0.89] in sitting position and 0.79 [0.73 to 0.84] in supine position) as well as the difficulty in assessing CSI (AUC [95% CI] = 0.92 [0.86 to 0.97] for easy, 0.84 [0.80 to 0.88] for moderate, and 0.80 [0.75 to 0.84] for difficult assessments).

Fig 3. Diagnostic value of radiographic CSI for AHF.

*Pneumonia was diagnosed at discharge. AHF, acute heart failure; AUC, area under the curve; COPD, chronic obstructive pulmonary disease; CSI, congestion score index.

Fig 4. Diagnostic performance of the radiographic CSI.

On the receiving operating characteristic curve for the association between CSI and AHF diagnosis, the red shaded region represents the 90% or greater specificity zone (CSI ≤ 1.3), whereas the green shaded region represents the 90% or greater sensitivity zone (CSI > 2.2). On the top portion of the figure, CXR panels illustrate typical examples of radiographies in the 3 zones, with CSI score of “1,” “2,” and “3,” respectively, from left to right. AHF, acute heart failure; AUC, area under the curve; BNP, b-type natriuretic peptide; CSI, congestion score index; CXR, chest radiography; NRI, net reclassification improvement.

AUC for the Brest score was 0.78 [0.75 to 0.81]. The combination of CSI and Brest score yielded a high AUC for AHF (AUC [95% CI] = 0.87 [0.85 to 0.90]).

Improvement in reclassification associated with acute heart failure diagnosis

The addition of CSI on top of the Brest score significantly improved reclassification of AHF diagnosis (NRI [95% CI] = 94.9 [83.5 to 106.2], p < 0.001) (Fig 4).

Furthermore, in patients with available BNP data (N = 496), CSI still significantly improved reclassification of AHF diagnosis on top of BNP and the Brest score (NRI [95% CI] = 55.0 [38.0 to 72.0], p < 0.001, delta AUC [95% CI] = 2.9 [0.6 to 5.2], p = 0.015) (Figs 4 and 5). The diagnostic value of the joint use of the Brest score and CSI was not significantly different than that of the joint use of the Brest score and BNP (NRI [95% CI] = 4.4 [−13.3 to 22.1], p = 0.63, delta AUC [95% CI] = −1.4 [−5.0 to 2.1], p = 0.42). In this subgroup, the Brest score had a moderate accuracy (AUC [95% CI] = 0.72 [0.75 to 0.81]), but the combination of CSI, BNP, and Brest score resulted in high diagnostic value for AHF (AUC [95% CI] = 0.85 [0.82 to 0.89]) (Fig 5).

Fig 5. Added values of CSI and BNP for the diagnosis of AHF on top of the Brest score in patients with available BNP (N = 496).

AHF, acute heart failure; AUC, area under the curve; BNP, b-type natriuretic peptide; CSI, congestion score index; NRI, net reclassification improvement.

Validation in external cohorts

In the validation cohorts (N = 224), more than a half of the patients (56.7%) were men, mean age was 75.8 ± 13.9 years, mean CSI was 1.85 ± 0.87, and 72.7% had a diagnosis of AHF at discharge. The diagnostic performance of CSI was externally validated (AUC [95% CI] = 0.75 [0.68 to 0.82]).


Our results show that pulmonary congestion quantified by a simple standardized CXR scoring can efficiently identify patients with AHF in the ED, consistently across the various subgroups (e.g., elderly, overweight, COPD, and pneumonia). Furthermore, CSI significantly improved the reclassification of AHF diagnosis on top of the recognized clinical diagnostic markers of AHF and natriuretic peptides. Our findings suggest that a semiquantified assessment of congestion on CXR could represent a readily available and clinically useful diagnostic tool for AHF in acute dyspneic patients in the ED.

Radiographic congestion score index as a diagnostic tool for acute heart failure

CXR exhibited a higher diagnostic performance for AHF than usually reported in the literature [1416]. Of note, previous reports assessed the diagnostic value of CXR using a single or combination of typical radiographic signs of congestion using a global evaluation of the lungs [15,16,3133] rather than a systematic approach, with lung segmentation as used in the CSI method. Our group recently showed that more severe pulmonary congestion, quantified by either CSI or lung ultrasound at admission, was associated with higher pulmonary artery systolic pressure [19,34]. This association of CSI with hemodynamic data emphasizes the mechanistic plausibility of our results.

Recent registry data have shown that approximately 20% of patients hospitalized for AHF had concomitant lung diseases such as pneumonia and COPD [3537], and these patients generally excluded in previous reports assessing the diagnostic value of CXR [16,17,38,39]. However, our subgroup analysis provided a remarkably homogenous diagnostic performance of CSI across various subgroups (i.e., elderly and BMI) and coexisting lung diseases (i.e., COPD and pneumonia). The only factor appearing to decrease the diagnostic value of CSI was cardiomegaly, possibly as a result of the overlapping of relevant information of the lung fields with cardiac silhouette in these patients.

Radiographic congestion score index and other diagnostic measurements: Clinical parameters and natriuretic peptide

In keeping with previous literature data [19,40], our results showed that patients with more severe pulmonary congestion were predominantly elderly, had higher BMI, more cardiovascular risk factors and comorbidities, severe congestion, poorer renal function, and higher inflammatory markers. In the latest guidelines, natriuretic peptide is recommended to rule out non-HF–related causes of acute dyspnea, although accumulated data suggested the overall diagnostic accuracy of natriuretic peptide [4145]. However, multiple comorbidity burdens (i.e., older age and poor renal function) may lead to diagnostic uncertainty in a sizeable proportion of dyspneic patients [7]. BNP, in addition, requires time to measure and is not always available in routine clinical practice. In cases with unequivocal diagnosis of AHF based on clinical parameters, prompt treatment approach is recommended rather than wait for its result [6]. In this regard, the Brest score, based on clinical parameters, may be a pragmatic tool as well as good diagnostic accuracy for diagnosing HF as previously shown [27]. However, in the current study, more than half of dyspneic patients have intermediate scores, suggesting that this clinical score may need to be complemented by more refined strategies in this relatively frequent “grey zone.” The unmet need in clinical practice, even when using the Brest score and natriuretic peptides, thus remains high.

In the current study, CSI was found to improve reclassification of AHF diagnosis on top of the Brest score and BNP. In addition, the combination of CSI and the Brest score improved the diagnostic value (AUC 0.81) to a similar degree of the combination of BNP and the Brest score (AUC 0.82). CSI also significantly improved diagnostic accuracy on top of the Brest score and BNP, and the combined used of these 3 parameters resulted in an AUC of 0.85. Taken together, these findings further strengthen that CSI may play a complementary role to the clinical model and natriuretic peptides in diagnosing AHF.

Clinical implications

An early accurate diagnosis and consequently a prompt appropriate management improve outcomes in AHF patients admitted to the ED [5,46,47]. Our results show that a standardized evaluation of CXR, using CSI, improves diagnosis performance and potentially the ability to swiftly manage AHF in the ED. The assessment of radiographic pulmonary congestion requires training period. However, this approach may be easily scalable since training for this assessment is fairly simple; the operator (AD) who evaluated all CXRs of this cohort efficiently acquired the technique in the context of this study in a matter of a few hours.

Recent studies showed the clinical utility of lung ultrasound to diagnose AHF in dyspneic patients with high specificity and high sensitivity [9,26,48], and its diagnostic accuracy was better than that of CXR [8,50]. Of note, these previous studies did not include quantitative assessment of radiographic pulmonary congestion. In any event, based on the promising results herein, multicenter trials may be warranted to assess the impact of the implementation of CSI on AHF diagnosis in patients with acute dyspnea. Furthermore, further study to compare diagnostic value between CSI on CXR and lung ultrasound is a worthy undertaking.

Limitations and strengths

The main limitation of our derivation cohort was its single-center and retrospective nature, although the external validation of our results may lead to generalize our findings. The overall proportion of AHF diagnosis was relatively low, which may explain the fact that a low number of patients had congestion signs (i.e., leg edema, rales, and jugular venous distention) and cardiovascular diseases. In our center (as in most centers in France), some dyspneic patients known to have HF and all patients with obvious evidence of myocardial ischemia were admitted directly in the intensive cardiac care unit/cardiology ward, not through the ED [49]. The proportion of AHF diagnosis possibly due to the healthcare system may influence our results. However, it should be noted that hospitalization rates for worsening HF in the ED declined over the past decades [50], which may result from the development of a disease management program to prevent urgent HF hospitalization [51]. Indeed, the proportion of AHF diagnosis in the present study was similar to that of dyspneic patients in other contemporary cohorts [44,52]. Ejection fraction was not recorded; thus, we did not evaluate the diagnostic accuracy of CSI across levels of ejection fraction. This parameter, however, is usually not a major determinant of decision-making for acute dyspneic management in the ED [6,12].

In the derivation cohort, we had no data on CSI in 138 (9.4%) patients who did not undergo CXR or had no available lung field to assess CSI. These patients, however, had better clinical status (i.e., younger age and less severe congestion), and only 8% of these patients (N = 11) were diagnosed with AHF (S1 Table). In addition, all CSI readings were performed blinded for other parameters and diagnoses, suggesting that this limitation is unlikely to have major influence on our findings.

CSI is a semiquantitative tool with some subjectivity. Although accurate and reproducible scoring was achieved after about 3-hour training period in the current study and 1 of our previous study [19], more evidence may be needed to ascertain the appropriate learning period.

Lastly, the assessment of CSI was difficult in 502 (37.7%) patients, which may limit its applicability in routine clinical practice. However, the diagnostic value of CSI persisted in these patients (AUC = 0.80, 0.75 to 0.84) and the difficulty in assessing CSI did not influence its diagnostic accuracy (Pinteraction = 0.13).


Our study shows that a semiquantified assessment of radiographic pulmonary congestion provided diagnostic value for AHF in dyspneic patients of similar magnitude to that of BNP. These results suggest that implementing radiographic CSI in the diagnostic approach to AHF in addition to clinical parameters and BNP measurement could benefit the management of AHF patients. Better use of CXR may provide an inexpensive, widely, and readily available method for AHF triage in the ED. Multicenter prospective studies are nonetheless needed to confirm the diagnostic value of radiographic CSI.

Supporting information

S1 Table. Baseline characteristics of patients with available and unavailable chest radiograph.


S2 Table. Patient characteristics across different discharge diagnoses.



  1. 1. Mebazaa A, Yilmaz MB, Levy P, Ponikowski P, Peacock WF, Laribi S, et al. Recommendations on pre-hospital & early hospital management of acute heart failure: a consensus paper from the Heart Failure Association of the European Society of Cardiology, the European Society of Emergency Medicine and the Society of Academic Emergency Medicine. Eur J Heart Fail. 2015;17(6):544–558. pmid:25999021
  2. 2. Maggioni AP, Dahlstrom U, Filippatos G, Chioncel O, Crespo Leiro M, Drozdz J, et al. EURObservational Research Programme: regional differences and 1-year follow-up results of the Heart Failure Pilot Survey (ESC-HF Pilot). Eur J Heart Fail. 2013;15(7):808–817. pmid:23537547
  3. 3. Khera R, Pandey A, Ayers CR, Agusala V, Pruitt SL, Halm EA, et al. Contemporary epidemiology of heart failure in fee-for-service medicare beneficiaries across healthcare settings. Circ Heart Fail. 2017;10(11). pmid:29129828
  4. 4. Research. NIfCO. National heart failure audit—April 2012–March 2013. Available from:
  5. 5. Matsue Y, Damman K, Voors AA, Kagiyama N, Yamaguchi T, Kuroda S, et al. Time-to-furosemide treatment and mortality in patients hospitalized with acute heart failure. J Am Coll Cardiol. 2017;69(25):3042–3051. pmid:28641794
  6. 6. Ray P, Birolleau S, Lefort Y, Becquemin MH, Beigelman C, Isnard R, et al. Acute respiratory failure in the elderly: etiology, emergency diagnosis and prognosis. Crit Care. 2006;10(3):R82. pmid:16723034
  7. 7. Roberts E, Ludman AJ, Dworzynski K, Al-Mohammad A, Cowie MR, McMurray JJ, et al. The diagnostic accuracy of the natriuretic peptides in heart failure: systematic review and diagnostic meta-analysis in the acute care setting. BMJ. 2015;350:h910. pmid:25740799
  8. 8. Pivetta E, Goffi A, Nazerian P, Castagno D, Tozzetti C, Tizzani P, et al. Lung ultrasound integrated with clinical assessment for the diagnosis of acute decompensated heart failure in the emergency department: a randomized controlled trial. Eur J Heart Fail. 2019;21(6):754–766. pmid:30690825
  9. 9. Pivetta E, Goffi A, Lupia E, Tizzani M, Porrino G, Ferreri E, et al. Lung Ultrasound-Implemented Diagnosis of Acute Decompensated Heart Failure in the ED: A SIMEU Multicenter Study. Chest. 2015;148(1):202–210. pmid:25654562
  10. 10. Girerd N, Seronde MF, Coiro S, Chouihed T, Bilbault P, Braun F, et al. Integrative Assessment of Congestion in Heart Failure Throughout the Patient Journey. JACC Heart Fail. 2018;6(4):273–285. pmid:29226815
  11. 11. Gheorghiade M, Follath F, Ponikowski P, Barsuk JH, Blair JE, Cleland JG, et al. Assessing and grading congestion in acute heart failure: a scientific statement from the acute heart failure committee of the heart failure association of the European Society of Cardiology and endorsed by the European Society of Intensive Care Medicine. Eur J Heart Fail. 2010;12(5):423–433. pmid:20354029
  12. 12. Chouihed T, Manzo-Silberman S, Peschanski N, Charpentier S, Elbaz M, Savary D, et al. Management of suspected acute heart failure dyspnea in the emergency department: results from the French prospective multicenter DeFSSICA survey. Scand J Trauma Resusc Emerg Med. 2016;24(1):112. pmid:27639971
  13. 13. Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JG, Coats AJ, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail. 2016;18(8):891–975. pmid:27207191
  14. 14. Maw AM, Hassanin A, Ho PM, McInnes MDF, Moss A, Juarez-Colunga E, et al. Diagnostic accuracy of point-of-care lung ultrasonography and chest radiography in adults with symptoms suggestive of acute decompensated heart failure: a systematic review and meta-analysis. JAMA Netw Open. 2019;2(3):e190703. pmid:30874784
  15. 15. Mueller-Lenke N, Rudez J, Staub D, Laule-Kilian K, Klima T, Perruchoud AP, et al. Use of chest radiography in the emergency diagnosis of acute congestive heart failure. Heart. 2006;92(5):695–696. pmid:16159971
  16. 16. Collins SP, Lindsell CJ, Storrow AB, Abraham WT. Prevalence of negative chest radiography results in the emergency department patient with decompensated heart failure. Ann Emerg Med. 2006;47(1):13–18. pmid:16387212
  17. 17. Hawkins NM, Petrie MC, Jhund PS, Chalmers GW, Dunn FG, McMurray JJ. Heart failure and chronic obstructive pulmonary disease: diagnostic pitfalls and epidemiology. Eur J Heart Fail. 2009;11(2):130–139. pmid:19168510
  18. 18. Kobayashi M, Watanabe M, Coiro S, Bercker M, Paku Y, Iwasaki Y, et al. Mid-term prognostic impact of residual pulmonary congestion assessed by radiographic scoring in patients admitted for worsening heart failure. Int J Cardiol. 2019. pmid:30770263
  19. 19. Kobayashi M, Bercker M, Huttin O, Pierre S, Sadoul N, Bozec E, et al. Chest X-ray quantification of admission lung congestion as a prognostic factor in patients admitted for worsening heart failure from the ICALOR cohort study. Int J Cardiol. 2019. pmid:31281047
  20. 20. Melenovsky V, Andersen MJ, Andress K, Reddy YN, Borlaug BA. Lung congestion in chronic heart failure: haemodynamic, clinical, and prognostic implications. Eur J Heart Fail. 2015;17(11):1161–1171. pmid:26467180
  21. 21. Chouihed T, Rossignol P, Bassand A, Duarte K, Kobayashi M, Jaeger D, et al. Diagnostic and prognostic value of plasma volume status at emergency department admission in dyspneic patients: results from the PARADISE cohort. Clin Res Cardiol. 2018. pmid:30370469
  22. 22. Chouihed T, Buessler A, Bassand A, Jaeger D, Virion JM, Nace L, et al. Hyponatraemia, hyperglycaemia and worsening renal function at first blood sample on emergency department admission as predictors of in-hospital death in patients with dyspnoea with suspected acute heart failure: retrospective observational analysis of the PARADISE cohort. BMJ Open. 2018;8(3):e019557. pmid:29602842
  23. 23. Agrinier N, Altieri C, Alla F, Jay N, Dobre D, Thilly N, et al. Effectiveness of a multidimensional home nurse led heart failure disease management program—a French nationwide time-series comparison. Int J Cardiol. 2013;168(4):3652–3658. pmid:23809709
  24. 24. Alla F, Agrinier N, Lavielle M, Rossignol P, Gonthier D, Boivin JM, et al. Impact of the interruption of a large heart failure regional disease management programme on hospital admission rates: a population-based study. Eur J Heart Fail. 2018;20(6):1066–1068. pmid:29660817
  25. 25. McMurray JJ, Adamopoulos S, Anker SD, Auricchio A, Bohm M, Dickstein K, et al. ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: The Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2012 of the European Society of Cardiology. Developed in collaboration with the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail. 2012;14(8):803–869. pmid:22828712
  26. 26. Buessler A, Chouihed T, Duarte K, Bassand A, Huot-Marchand M, Gottwalles Y, et al. Accuracy of several lung ultrasound methods for the diagnosis of acute heart failure in the emergency department: A multicenter prospective study. Chest. 2019.
  27. 27. Basset A, Nowak E, Castellant P, Gut-Gobert C, Le Gal G, L’Her E. Development of a clinical prediction score for congestive heart failure diagnosis in the emergency care setting: The Brest score. Am J Emerg Med. 2016;34(12):2277–2283. pmid:27599400
  28. 28. Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med. 2016;35(2):214–226. pmid:26553135
  29. 29. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF III, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–612. pmid:19414839
  30. 30. Uno H, Tian L, Cai T, Kohane IS, Wei LJ. A unified inference procedure for a class of measures to assess improvement in risk prediction systems with survival data. Stat Med. 2013;32(14):2430–2442. pmid:23037800
  31. 31. Knudsen CW, Omland T, Clopton P, Westheim A, Abraham WT, Storrow AB, et al. Diagnostic value of B-Type natriuretic peptide and chest radiographic findings in patients with acute dyspnea. Am J Med. 2004;116(6):363–368. pmid:15006584
  32. 32. Sartini S, Frizzi J, Borselli M, Sarcoli E, Granai C, Gialli V, et al. Which method is best for an early accurate diagnosis of acute heart failure? Comparison between lung ultrasound, chest X-ray and NT pro-BNP performance: a prospective study. Intern Emerg Med. 2017;12(6):861–869. pmid:27401330
  33. 33. Shochat M, Shotan A, Trachtengerts V, Blondheim DS, Kazatsker M, Gurovich V, et al. A novel radiological score to assess lung fluid content during evolving acute heart failure in the course of acute myocardial infarction. Acute Card Care. 2011;13(2):81–86. pmid:21517671
  34. 34. Coiro S, Porot G, Rossignol P, Ambrosio G, Carluccio E, Tritto I, et al. Prognostic value of pulmonary congestion assessed by lung ultrasound imaging during heart failure hospitalisation: A two-centre cohort study. Sci Rep. 2016;6:39426. pmid:27995971
  35. 35. Chioncel O, Mebazaa A, Harjola VP, Coats AJ, Piepoli MF, Crespo-Leiro MG, et al. Clinical phenotypes and outcome of patients hospitalized for acute heart failure: the ESC Heart Failure Long-Term Registry. Eur J Heart Fail. 2017;19(10):1242–1254. pmid:28463462
  36. 36. Arrigo M, Gayat E, Parenica J, Ishihara S, Zhang J, Choi DJ, et al. Precipitating factors and 90-day outcome of acute heart failure: a report from the intercontinental GREAT registry. Eur J Heart Fail. 2017;19(2):201–208. pmid:27790819
  37. 37. Jobs A, Simon R, de Waha S, Rogacev K, Katalinic A, Babaev V, et al. Pneumonia and inflammation in acute decompensated heart failure: a registry-based analysis of 1939 patients. Eur Heart J Acute Cardiovasc Care. 2018;7(4):362–370. pmid:28357890
  38. 38. Baker K, Mitchell G, Thompson AG, Stieler G. Comparison of a basic lung scanning protocol against formally reported chest x-ray in the diagnosis of pulmonary oedema. Australas J Ultrasound Med. 2013;16(4):183–189. pmid:28191195
  39. 39. Hublitz UF, Shapiro JH. Atypical pulmonary patterns of congestive failure in chronic lung disease. The influence of pre-existing disease on the appearance and distribution of pulmonary edema. Radiology. 1969;93(5):995–1006. pmid:5350699
  40. 40. Platz E, Lewis EF, Uno H, Peck J, Pivetta E, Merz AA, et al. Detection and prognostic value of pulmonary congestion by lung ultrasound in ambulatory heart failure patients. Eur Heart J. 2016;37(15):1244–1251. pmid:26819225
  41. 41. Krauser DG, Chen AA, Tung R, Anwaruddin S, Baggish AL, Januzzi JL Jr. Neither race nor gender influences the usefulness of amino-terminal pro-brain natriuretic peptide testing in dyspneic subjects: a ProBNP Investigation of Dyspnea in the Emergency Department (PRIDE) substudy. J Card Fail. 2006;12(6):452–457. pmid:16911912
  42. 42. Anwaruddin S, Lloyd-Jones DM, Baggish A, Chen A, Krauser D, Tung R, et al. Renal function, congestive heart failure, and amino-terminal pro-brain natriuretic peptide measurement: results from the ProBNP Investigation of Dyspnea in the Emergency Department (PRIDE) Study. J Am Coll Cardiol. 2006;47(1):91–97. pmid:16386670
  43. 43. Bayes-Genis A, Lloyd-Jones DM, van Kimmenade RR, Lainchbury JG, Richards AM, Ordonez-Llanos J, et al. Effect of body mass index on diagnostic and prognostic usefulness of amino-terminal pro-brain natriuretic peptide in patients with acute dyspnea. Arch Intern Med. 2007;167(4):400–407. pmid:17325303
  44. 44. Januzzi JL Jr, Chen-Tournoux AA, Christenson RH, Doros G, Hollander JE, Levy PD, et al. N-Terminal Pro-B-Type Natriuretic Peptide in the Emergency Department: The ICON-RELOADED Study. J Am Coll Cardiol. 2018;71(11):1191–1200. pmid:29544601
  45. 45. Daniels LB, Maisel AS. Natriuretic peptides. J Am Coll Cardiol. 2007;50(25):2357–2368. pmid:18154959
  46. 46. Maisel AS, Peacock WF, McMullin N, Jessie R, Fonarow GC, Wynne J, et al. Timing of immunoreactive B-type natriuretic peptide levels and treatment delay in acute decompensated heart failure: an ADHERE (Acute Decompensated Heart Failure National Registry) analysis. J Am Coll Cardiol. 2008;52(7):534–540. pmid:18687247
  47. 47. Peacock WF, Emerman C, Costanzo MR, Diercks DB, Lopatin M, Fonarow GC. Early vasoactive drugs improve heart failure outcomes. Congest Heart Fail. 2009;15(6):256–264. pmid:19925503
  48. 48. Martindale JL, Wakai A, Collins SP, Levy PD, Diercks D, Hiestand BC, et al. Diagnosing Acute Heart Failure in the Emergency Department: A Systematic Review and Meta-analysis. Acad Emerg Med. 2016;23(3):223–242. pmid:26910112
  49. 49. Logeart D, Isnard R, Resche-Rigon M, Seronde MF, de Groote P, Jondeau G, et al. Current aspects of the spectrum of acute heart failure syndromes in a real-life setting: the OFICA study. Eur J Heart Fail. 2013;15(4):465–476. pmid:23186936
  50. 50. Schmidt M, Ulrichsen SP, Pedersen L, Bøtker HE, Sørensen HT. Thirty-year trends in heart failure hospitalization and mortality rates and the prognostic impact of co-morbidity: a Danish nationwide cohort study. Eur J Heart Fail. 2016;18(5):490–499. pmid:26868921
  51. 51. Bradley EH, Curry L, Horwitz LI, Sipsma H, Wang Y, Walsh MN, et al. Hospital strategies associated with 30-day readmission rates for patients with heart failure. Circ Cardiovasc Qual Outcomes. 2013;6(4):444–450. pmid:23861483
  52. 52. Ibrahim I, Kuan WS, Frampton C, Troughton R, Liew OW, Chong JP, et al. Superior performance of N-terminal pro brain natriuretic peptide for diagnosis of acute decompensated heart failure in an Asian compared with a Western setting. Eur J Heart Fail. 2017;19(2):209–217. pmid:27620387